class_docstr: This class defines image-level normalisation by subtracting
  foreground mean intensity value and dividing by standard deviation
code: def __init__(self, image_name, binary_masking_func=None):
      self.image_name = image_name
      super(MeanVarNormalisationLayer, self).__init__(
          name="mean_var_norm"
      )
      self.binary_masking_func = None
      if binary_masking_func is not None:
          assert isinstance(binary_masking_func, BinaryMaskingLayer)
          self.binary_masking_func = binary_masking_func
  
file_path: NiftyNet/niftynet/layer/mean_variance_normalisation.py
imports: from __future__ import absolute_import, print_function
  import numpy
  import numpy.ma
  from niftynet.layer.base_layer import Layer
  from niftynet.layer.binary_masking import BinaryMaskingLayer
label_desc: Use image node_id 1 to create a new MeanVarNormalisationLayer object from inherited base classes: Layer with example: obj = MeanVarNormalisationLayer(image_name, binary_masking_func)